Layout planning is an important practical problem for manufacturing companies. In today's market conditions —characterized with continuously changing product portfolio and shortening product lifecycles— frequent...
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Layout planning is an important practical problem for manufacturing companies. In today's market conditions —characterized with continuously changing product portfolio and shortening product lifecycles— frequent reconfiguration is requested if the primary goal for the company is to remain competitive. The key to win customers is to widen the product portfolio and customize the products, however, this leads to the problem that the manufacturing system has to be re-organized several times during its life cycle that requires solving design problems frequently. In the paper, a novel layout planning method is introduced that can be applied efficiently to solve real industrial problems. The method applies automated simulation model building to create the different layouts. It focuses on minimizing the objective function that is specified according to the predefined key performance indicators (KPI). The solution is a hybrid optimization method, in which evaluation of the layout alternatives is done by simulation and the improvement of the solution is performed by a near-to-optimal search algorithm. The optimization is separated from the simulation model in order to boost the computations. Important advantage of the solution is the efficiency consideration of stochastic parameters that improve the applicability of the results.
Hepatitis C is a major public health problem in the United States and worldwide. Outbreaks of hepatitis C virus (HCV) infections associated with unsafe injection practices, drug diversion, and other exposures to blood...
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Our starting point is the ascertainment that D-optimal input signals recently considered by the same authors [12] can be too dangerous for applying them to real life system identification. The reason is that they grow...
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Typical learning factories are characterized by selective simplification or scaling-down of complex and large-scale production processes, while also safely containing risks in the case of process failures inherent to ...
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Typical learning factories are characterized by selective simplification or scaling-down of complex and large-scale production processes, while also safely containing risks in the case of process failures inherent to experimental and didactic activities. The variety of aspects preserved by these scaled-down environments allow different approaches to be taken in research and education. The paper compares two facilities, at TU Wien and at MTA SZTAKI in Budapest, respectively, and highlights differences in their modes of operation, the resulting variations of course-based vs. project-based didactic approaches, as well as their place in technical higher education.
The paper assesses possible approaches to decomposition and parallelization of basic linear programming algorithms, including: Dantzig-Wolfe, Benders, augmented Lagrangian, revised simplex and primal-dual interior poi...
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Train safety monitoring and fault diagnosis are critically important because of the disastrous results caused by train collisions and derailments. Train safety protection sensors network is capable of autonomously mon...
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Fuzzy theory was motivated by the need to create human-like solutions that allow representing vagueness and uncertainty that exist in the real-world. These capabilities have been recently further enhanced by deep lear...
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ISBN:
(纸本)9781509006274
Fuzzy theory was motivated by the need to create human-like solutions that allow representing vagueness and uncertainty that exist in the real-world. These capabilities have been recently further enhanced by deep learning since it allows converting complex relation between data into knowledge. In this paper, we present a novel Deep-Neuro-Fuzzy strategy for unsupervised estimation of the interaction forces in Robotic Assisted Minimally Invasive scenarios. In our approach, the capability of Neuro-Fuzzy systems for handling visual uncertainty, as well as the inherent imprecision of real physical problems, is reinforced by the advantages provided by Deep Learning methods. Experiments conducted in a realistic setting have demonstrated the superior performance of the proposed approach over existing alternatives. More precisely, our method increased the accuracy of the force estimation and compared favorably to existing state of the art approaches, offering a percentage of improvement that ranges from about 35% to 85%.
Cluster analysis is important in scientific and industrial fields. In this study, we proposed a novel chaotic biogeography-based optimization(CBBO) method, and applied it in centroid-based clustering methods. The resu...
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Cluster analysis is important in scientific and industrial fields. In this study, we proposed a novel chaotic biogeography-based optimization(CBBO) method, and applied it in centroid-based clustering methods. The results over three types of simulation data showed that this proposed CBBO method gave better performance than chaotic particle swarm optimization, genetic algorithm, firefly algorithm, and quantum-behaved particle swarm optimization. In all, our CBBO method is effective in centroid-based clustering.
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